US8325090B2 - Computer-implemented method and system for locating an indoor object - Google Patents

Computer-implemented method and system for locating an indoor object Download PDF

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US8325090B2
US8325090B2 US12/508,040 US50804009A US8325090B2 US 8325090 B2 US8325090 B2 US 8325090B2 US 50804009 A US50804009 A US 50804009A US 8325090 B2 US8325090 B2 US 8325090B2
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motion sensor
coordinate
training
covariance matrix
fused
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Ren-Chyuan LUO
Chien-Chung Chen
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National Taiwan University NTU
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/74—Systems using reradiation of radio waves, e.g. secondary radar systems; Analogous systems
    • G01S13/82—Systems using reradiation of radio waves, e.g. secondary radar systems; Analogous systems wherein continuous-type signals are transmitted
    • G01S13/825—Systems using reradiation of radio waves, e.g. secondary radar systems; Analogous systems wherein continuous-type signals are transmitted with exchange of information between interrogator and responder
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
    • G01S13/878—Combination of several spaced transmitters or receivers of known location for determining the position of a transponder or a reflector
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0257—Hybrid positioning
    • G01S5/0263—Hybrid positioning by combining or switching between positions derived from two or more separate positioning systems
    • G01S5/0264—Hybrid positioning by combining or switching between positions derived from two or more separate positioning systems at least one of the systems being a non-radio wave positioning system

Definitions

  • a conventional system for locating an indoor object such as that disclosed in “A pyroelectric infrared sensor-based indoor location-aware system (PILAS) for the smart home,” Lee et al., IEEE Transactions on Consumer Electronics , Volume 52, Issue 4, November 2006 Page(s): 1311-1317, utilizes a plurality of pyroelectric infrared (PIR) sensors to locate the indoor object.
  • PIR pyroelectric infrared
  • the PILAS is unable to determine whether there are two indoor objects moving in sensing areas A and B, respectively, or only one indoor object moving in overlapped region AB of sensing areas A and B.
  • RSS Received Signal Strength
  • the RSS approach utilizes at least three reference node RF transceivers, each of which is placed at a pre-determined position, and a blind node RF transceiver carried by the indoor object to locate the indoor object.
  • the RSS approach estimates the position of the indoor object according to at least three RSS signals, each of which is related to a distance between a respective one of the reference node RF transceivers and the blind node RF transceiver.
  • the RSS approach is disadvantageous in that the propagation of radio signals is easily affected by indoor obstacles, such as furniture and people in the indoor area, as well as by other electronic devices, which can result in estimating error and Non-Line-of-Sight (NLOS) error.
  • NLOS error is attributed to signal interference caused by indoor obstacles, and gets worse when the number of indoor obstacles is increased.
  • the object of the present invention is to provide a computer-implemented method and a system for locating an indoor object.
  • the RF coordinate signal carries information of an RF coordinate on a coordinate plane estimating a position of the indoor object.
  • the method comprises: a) receiving the RF coordinate signal and the detecting signal; b) providing information of a located region on the coordinate plane with reference to the RF coordinate and the detecting signal; c) obtaining a motion sensor coordinate on the coordinate plane according to the information of the located region; d) providing weights for the RF coordinate and the motion sensor coordinate; e) applying the weights to the RF coordinate and the sensor coordinate, respectively; and f) combining the weighted RF coordinate and the weighted sensor coordinate to generate a fused coordinate on the coordinate plane corresponding to the position of the indoor object.
  • a system for locating an indoor object comprises: an RF coordinate estimation unit configured to output an RF coordinate signal carrying information of an RF coordinate on a coordinate plane estimating a position of the indoor object; a motion sensor coordinate estimation unit including a motion sensor array and a localization module, the motion sensor array including at least one motion sensor configured to output a detecting signal upon detection of movement of the indoor object, the localization module being configured to receive the RF coordinate signal and the detecting signal, to provide information of a located region on the coordinate plane with reference to the RF coordinate and the detecting signal, and to obtain a motion sensor coordinate on the coordinate plane according to the information of the located region; and a fusion unit including a weight generation module and a fused coordinate calculation module.
  • the weight generation module is configured to provide weights for the RF coordinate and the motion sensor coordinate.
  • the fused coordinate calculation module is configured to receive the RF coordinate and the motion sensor coordinate, to apply the weights to the RF coordinate and the motion sensor coordinate, respectively, and to combine the weighted RF coordinate and the weighted motion sensor coordinate to generate a fused coordinate on the coordinate plane corresponding to the position of the indoor object.
  • FIG. 1 is a schematic view to illustrate a conventional PILAS used for locating indoor objects
  • FIG. 2 is a block diagram to illustrate the preferred embodiment of a system for locating an indoor object according to the present invention
  • FIG. 3 is a flow chart to illustrate the preferred embodiment of a method for locating an indoor object according to the present invention
  • FIG. 5A is a schematic view to illustrate an example of how the indoor object is located by the system of the preferred embodiment when there is only one indoor object in a room;
  • FIG. 5B is a schematic view to illustrate an example of how the indoor object is located by the system of the preferred embodiment when there are two indoor objects in the room;
  • FIG. 5D is a schematic view to illustrate an example of how the indoor object is located by the system of the preferred embodiment when there are two indoor objects in the room and an RF coordinate is contaminated by environmental factors, such as indoor objects.
  • FIG. 2 in combination with FIG. 5A , illustrates the preferred embodiment of a system 2 for locating an indoor object (such as a human) in a room (not shown).
  • the system 2 includes an RF coordinate estimation unit 21 configured to output an RF coordinate signal carrying information of an RF coordinate on a coordinate plane (such as Cartesian plane) estimating a position of the indoor object; a motion sensor coordinate estimation unit 22 configured to output a motion sensor coordinate on the coordinate plane estimating the position of the indoor object; and a fusion unit 23 configured to receive the RF coordinate and the motion sensor coordinate, and to fuse the RF coordinate and the motion sensor coordinate to generate a fused coordinate on the coordinate plane corresponding to the position of the indoor object.
  • the system 2 is implemented using a wireless network (not shown) for signal transmission and reception.
  • the RF coordinate estimation unit 21 includes at least three reference node RF transceivers 211 , a blind node RF transceiver 212 , and a localization module 213 .
  • Each of the reference node RF transceivers 211 corresponds to a pre-determined reference node coordinate on the coordinate plane, and is placed at a pre-determined position (not shown) in the room.
  • the blind node RF transceiver 212 is carried by the indoor object.
  • the localization module 213 is configured to receive Received Signal Strength Index (RSSI) values, which are related respectively to the distances between said at least three of the reference node RF transceivers 211 and the blind node RF transceiver 212 , from the blind node RF transceiver 212 , and to generate the RF coordinate on the coordinate plane according to the pre-determined reference node coordinates on the coordinate plane and the RSSI value.
  • RSSI Received Signal Strength Index
  • the reference node RF transceivers 211 are CC2420 chips
  • the blind node RF transceiver 212 is a CC2431 chip.
  • the CC2420 chip is a ZigBeeTM protocol RF transceiver (compliant with the IEEE 802.15.4 standard) available from Texas InstrumentsTM (TI)
  • the CC2431 is a System-On-Chip (SOC) that includes a location engine and is based on the ZigBeeTM protocol.
  • SOC System-On-Chip
  • the functions and operations of the RF coordinate estimation unit 21 are readily appreciated by those skilled in the art. Therefore, further details of the RF coordinate estimation unit 21 are omitted herein for the sake of brevity.
  • the fusion unit 23 includes a weight generation module 231 and a fused coordinate calculation module 232 .
  • the weight generation module 231 is configured to provide weights for the RF coordinate and the motion sensor coordinate.
  • the fused coordinate calculation module 232 is configured to receive the RF coordinate and the motion sensor coordinate, to apply the weights to the RF coordinate and the motion sensor coordinate, respectively, and to combine the weighted RF coordinate and the weighted motion sensor coordinate to generate the fused coordinate on the coordinate plane corresponding to the position of the indoor object.
  • the functions and operations of the localization module 222 of the motion sensor coordinate estimation unit 22 and the weight generation module 231 and the fused coordinate calculation module 232 of the fusion unit 23 can be performed by a processor of a computer (not shown).
  • the present invention will be described in more detail with reference to a computer-implemented method for locating the indoor object.
  • the method according to the present invention includes the following steps.
  • the weight generation module 231 of the fusion unit 23 adopts a Covariance Intersection (CI) estimator for offline training and generating the weights for the RF coordinate and the motion sensor coordinate.
  • the CI estimator may be a generalized Kalman filter.
  • the main advantage of using the CI estimator is that it permits data fusion to be performed with probabilistically defined estimates, i.e., a reasonable data fusion result can be achieved without knowing the correlation among those estimates. Hence, no assumptions of dependency between two values are needed by the CI estimator when it fuses the values together.
  • the weight generation module 231 provides a plurality of actual coordinates corresponding to a plurality of training patterns, respectively, a plurality of RF training coordinates corresponding to the training patterns, respectively, and a plurality of motion sensor training coordinates corresponding to the training patterns, respectively.
  • the actual coordinates, the RF training coordinates, and the motion sensor training coordinates can be pre-stored in a computer-readable storage medium (not shown).
  • the weight generation module 231 computes an RF coordinate covariance matrix P aa according to the actual coordinates and the RF training coordinates ⁇ , and then computes a motion sensor coordinate covariance matrix P bb according to the actual coordinates and the motion sensor training coordinates b .
  • the RF coordinate covariance matrix P aa and the motion sensor coordinate covariance matrix P bb are calculated according to the following formulas (1) and (2).
  • the weight generation module 231 gives a training weight W.
  • the weight generation module 231 determines whether the fused coordinate covariance matrix P cc satisfies a pre-determined optimal criterion (such as whether a minimum trace value of the fused coordinate covariance matrix P cc is found, or whether a minimum determinant value of the fused coordinate covariance matrix P cc is found). If the fused coordinate covariance matrix P cc satisfies the pre-determined optimal criteria, then the flow continues to sub-step 317 , otherwise, the flow returns to sub-step 314 .
  • a pre-determined optimal criterion such as whether a minimum trace value of the fused coordinate covariance matrix P cc is found, or whether a minimum determinant value of the fused coordinate covariance matrix P cc is found.
  • sub-steps 314 ⁇ 316 can be conducted using functions of mathematic tools, such as the MATLABTM fminbnd function.
  • step 31 is performed only one time, and is required to be performed again when any of the motion sensors of the motion sensor array 221 is reinstalled at a different position, or any of the reference node RF transceivers 211 is replaced.
  • the RF coordinate estimation unit 21 generates and transmits the RF coordinate signal carrying information of the RF coordinate on the coordinate plane. If there are more than one indoor objects in the room, the RF coordinate estimation unit 21 generates an RF coordinate signal for each of the indoor objects.
  • the motion sensor array 221 of the motion sensor coordinate estimation unit 22 generates the detecting signal upon detection of movement of the indoor object, and transmits the detecting signal via the wireless network to the localization module 222 of the motion sensor coordinate estimation unit 22 .
  • the localization module 222 of the motion sensor coordinate estimation unit 22 receives the RF coordinate signal and the detecting signal.
  • the localization module 222 When only one detecting signal is received, the localization module 222 provides information of a predetermined sensing area of the motion sensor array 221 on the coordinate plane in which the RF coordinate falls, defines the predetermined sensing area as the located region, and takes a geometric center of the predetermined sensing area as the motion sensor coordinate.
  • the localization module 222 provides information of a plurality of pre-determined sensing areas of the motion sensor array 221 on the coordinate plane, such that at least two of the pre-determined sensing areas overlap each other so as to divide the pre-determined sensing areas into an overlapped region and non-overlapped regions, determines one of the non-overlapped regions and the overlapped region, in which the RF coordinate falls, as the located region, and takes a geometric center of one of the predetermined sensing areas corresponding to one of the non-overlapped regions when the RF coordinate falls in said one of the non-overlapped regions as the motion sensor coordinate, and takes a geometric center of the overlapped region when the RF coordinate falls in the overlapped region as the motion sensor coordinate.
  • the motion sensor array 221 of the motion sensor coordinate estimation unit 22 includes three motion sensors d, e, and f installed on the ceiling 4 in the room and operable to detect an indoor object(s) within the predetermined sensing areas D, E, and F, respectively.
  • the information of the predetermined sensing areas D, E, and F (such as the geometric centers of the predetermined sensing areas D, E, and F) and the overlapped regions DE and EF can be pre-stored in the computer readable storage medium (not shown).
  • FIG. 5A illustrates the example for the situation when there is only one indoor object in the room.
  • the localization module 222 of the motion sensor coordinate estimation unit 22 receives one RF coordinate signal carrying information of a RF coordinate 51 , and two detecting signals of motion sensors d and e upon detection of movement of the indoor S object (step 34 ), then provides information of the predetermined sensing areas D, E and the overlapped region DE, and determines the overlapped region DE in which the RF coordinate 51 falls (in this situation, the RF coordinate 51 is nearest to a geometric center 52 of the overlapped region DE) as the located region (step 35 ).
  • the localization module 222 of the motion sensor coordinate estimation unit 22 then takes the geometric center 52 of the overlapped region DE as the motion sensor coordinate for estimating the position of the indoor object (step 36 ) and outputs the motion sensor coordinate to the fused coordinate calculation module 232 .
  • FIG. 5B illustrates the example for the situation when there are two indoor objects in the room.
  • the localization module 222 of the motion sensor coordinate estimation unit 22 receives two RF coordinate signals carrying information of two RF coordinates 53 and 54 , respectively, and two detecting signals of motion sensors d and e upon detection of the indoor objects (step 34 ), and then provides information of the predetermined sensing areas D, E, and the overlapped region DE and determines the overlapped region DE in which the RF coordinate 53 falls and a non-overlapped region D 1 in which the RF coordinate 54 falls as the located regions (step 35 ).
  • FIG. 5C illustrates the example for the situation when there are three indoor objects in the room.
  • the localization module 222 of the motion sensor coordinate estimation unit 22 takes the geometric center 52 of the overlapped region DE in which the RF coordinate 56 falls, the geometric center 55 of the predetermined sensing area D in which the RF coordinate 57 falls, and a geometric center 59 of the predetermined sensing area F in which the RF coordinate 58 falls, as the motion sensor coordinates for estimating the positions of the indoor objects, respectively, and outputs the motion sensor coordinates to the fused coordinate calculation module 232 .
  • the estimating result (the RF coordinate) of the RF coordinate estimation unit 21 ( FIG. 2 ) is prone to error, i.e., deviates from the actual coordinate of the indoor object, as a result of the influence of indoor obstacles.
  • the localization module 222 of the motion sensor coordinate estimation unit 22 receives two RF coordinate signals carrying information of two RF coordinates 61 and 62 , and two detecting signals of motion sensors d and e upon detection of movement of the indoor objects.
  • both the RF coordinates 61 and 62 tall in the predetermined sensing area D, with the RF coordinate 61 close to the overlapped region DE.
  • the fused coordinate calculation module 232 of the fusion unit 23 receives the RF coordinate [x RF — i y RF — i ] T and the motion sensor coordinate [x Motion — i y Motion — i ] T , and applies the weights to the RF coordinate [x RF — i y RF — i ] T and the motion sensor coordinate [x Motion — i y Motion — i ] T .
  • the fused coordinate calculation module 232 of the fusion unit 23 sums the weighted RF coordinate and the weighted the motion sensor coordinate to generate the fused coordinate [x Fused — i y Fused — i ] T on the coordinate plane corresponding to the position of the indoor object.
  • the fused coordinate [x Fused — i y Fused — i ] T is calculated according to the following formula (5).

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140141798A1 (en) * 2012-11-20 2014-05-22 Novatek Microelectronics Corp. Method and Computer System for Obtaining Weighted Geometric Dilution of Precision Closed Form
US9173067B2 (en) 2013-12-02 2015-10-27 At&T Intellectual Property I, L.P. Method and apparatus for performing a passive indoor localization of a mobile endpoint device
US10684349B2 (en) * 2013-10-02 2020-06-16 Nextome S.R.L. Enhanced indoor localization method
EP3624077B1 (en) 2018-09-17 2023-11-01 Zumtobel Lighting GmbH Object detection sensor network for calculating a motion path of an object

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8174931B2 (en) 2010-10-08 2012-05-08 HJ Laboratories, LLC Apparatus and method for providing indoor location, position, or tracking of a mobile computer using building information
CN102740453B (zh) * 2011-04-14 2015-05-20 鸿富锦精密工业（深圳）有限公司 具定位功能的无线网络接入设备及定位方法
US9274208B2 (en) 2011-06-29 2016-03-01 Worcester Polytechnic Institute Enhanced RF location methods and systems
CN103728645B (zh) * 2012-10-10 2016-06-08 航天数联信息技术（深圳）有限公司 一种室内外定位系统及其定位方法
CN103399298B (zh) * 2013-07-30 2015-12-02 中国科学院深圳先进技术研究院 一种基于光强度的多传感器室内定位装置与方法
US10254378B1 (en) * 2016-07-05 2019-04-09 Phunware, Inc. Mobile device localization based on relative received signal strength indicators
CN106597882B (zh) * 2016-11-29 2019-10-15 深圳市极成光电有限公司 基于传感器的智能定位感应示教方法、装置和系统
CN107610173B (zh) * 2017-08-11 2021-04-02 北京圣威特科技有限公司 一种实时定位方法和装置
WO2020012144A1 (en) * 2018-07-12 2020-01-16 Source to Site Accessories Limited Determining presence of a person

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6738628B1 (en) * 1999-09-03 2004-05-18 International Business Machines Corporation Electronic physical asset tracking
US20090189810A1 (en) * 2008-01-24 2009-07-30 Broadcom Corporation Weighted aiding for positioning systems
US7904244B2 (en) * 2003-11-18 2011-03-08 Sarimo Technologies, LLC Determining a location or position using information from multiple location and positioning technologies and applications using such a determined location or position

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7257411B2 (en) * 2002-12-27 2007-08-14 Ntt Docomo, Inc. Selective fusion location estimation (SELFLOC) for wireless access technologies
US7151454B2 (en) * 2003-01-02 2006-12-19 Covi Technologies Systems and methods for location of objects
WO2007018523A2 (en) * 2004-07-28 2007-02-15 Sarnoff Corporation Method and apparatus for stereo, multi-camera tracking and rf and video track fusion
US7266477B2 (en) * 2005-06-22 2007-09-04 Deere & Company Method and system for sensor signal fusion

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6738628B1 (en) * 1999-09-03 2004-05-18 International Business Machines Corporation Electronic physical asset tracking
US7904244B2 (en) * 2003-11-18 2011-03-08 Sarimo Technologies, LLC Determining a location or position using information from multiple location and positioning technologies and applications using such a determined location or position
US20090189810A1 (en) * 2008-01-24 2009-07-30 Broadcom Corporation Weighted aiding for positioning systems

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
Gwon, Y. et al, "Robust Indoor Location Estimation of Stationary and Mobile Users," IEEE Infocom, 2004, pp. 1-11. *
King et al, "A Location System Based on Sensor Fusion: Research Areas and Software Architecture," Univeristy of Mannheim, May 2005. *
Krishnan, P. et al, "A System for LEASE: Location Estimation Assisted by Stationary Emitters for Indoor RF Wireless Networks," IEEE Infocom 2004, pp. 1-10. *
Lee et al, "A Pyroelectric Infrared Sensor-Based Indoor Location Aware System For the Smart Home," IEEE Trans. on Consumer Electronics, vol. 52, #4, Nov. 2006. *
Niehsen et al, "Information Fusion based on Fast Covariance Intersection Filtering," Proc. of the 5th International Conf. on Information Fusion, Jan. 2002, pp. 901-904. *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140141798A1 (en) * 2012-11-20 2014-05-22 Novatek Microelectronics Corp. Method and Computer System for Obtaining Weighted Geometric Dilution of Precision Closed Form
US8971921B2 (en) * 2012-11-20 2015-03-03 Novatek Microelectronics Corp. Method and computer system for obtaining weighted geometric dilution of precision closed form
US10684349B2 (en) * 2013-10-02 2020-06-16 Nextome S.R.L. Enhanced indoor localization method
US9173067B2 (en) 2013-12-02 2015-10-27 At&T Intellectual Property I, L.P. Method and apparatus for performing a passive indoor localization of a mobile endpoint device
US9723586B2 (en) 2013-12-02 2017-08-01 At&T Intellectual Property I, L.P. Method and apparatus for performing a passive indoor localization of a mobile endpoint device
US10104634B2 (en) 2013-12-02 2018-10-16 At&T Intellectual Property I, L.P. Method and apparatus for performing a passive indoor localization of a mobile endpoint device
EP3624077B1 (en) 2018-09-17 2023-11-01 Zumtobel Lighting GmbH Object detection sensor network for calculating a motion path of an object

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